TY - GEN
T1 - Load-balancing adaptive clustering refinement algorithm for wireless sensor network clusters
AU - Oren, Gal
AU - Barenboim, Leonid
AU - Levin, Harel
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Energy efficiency is a crucial performance metric in sensor networks, imminently determining the network lifetime. Consequently, a key objective in WSN is to improve overall energy efficiency to extend the network lifetime. Its conservation influences the topology design of many WSN-based systems, especially the clustering of the network. Unlike other WSN clustering algorithms, that do not re-cluster the network after deployment, our hypothesis is that it is advisable, in terms of prolonging the network lifetime, to adaptively re-cluster specific regions that are triggered significantly more than other regions in the network. By doing so, it is possible to minimize or even prevent the premature death of CHs, which are heavily burdened with sensing and transmitting actions – much more than other parts of the WSN. In order to do so we introduce the Adaptive Clustering Refinement (ACR) algorithm, which is based on the Adaptive Mesh Refinement algorithm by Berger and Oliger [14] and the Hierarchical Control Clustering algorithm by Banerjee and Khuller [13]. We prove that the ACR algorithm complexity is linear in the total size of the graph, and that we manage to optimize the WSN cluster connectivity and prolong its lifetime. We also devise a local version of the algorithm with improved complexity.
AB - Energy efficiency is a crucial performance metric in sensor networks, imminently determining the network lifetime. Consequently, a key objective in WSN is to improve overall energy efficiency to extend the network lifetime. Its conservation influences the topology design of many WSN-based systems, especially the clustering of the network. Unlike other WSN clustering algorithms, that do not re-cluster the network after deployment, our hypothesis is that it is advisable, in terms of prolonging the network lifetime, to adaptively re-cluster specific regions that are triggered significantly more than other regions in the network. By doing so, it is possible to minimize or even prevent the premature death of CHs, which are heavily burdened with sensing and transmitting actions – much more than other parts of the WSN. In order to do so we introduce the Adaptive Clustering Refinement (ACR) algorithm, which is based on the Adaptive Mesh Refinement algorithm by Berger and Oliger [14] and the Hierarchical Control Clustering algorithm by Banerjee and Khuller [13]. We prove that the ACR algorithm complexity is linear in the total size of the graph, and that we manage to optimize the WSN cluster connectivity and prolong its lifetime. We also devise a local version of the algorithm with improved complexity.
KW - Adaptive clustering refinement
KW - Energy optimization
KW - Networks connectivity
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85021769523&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61382-6_13
DO - 10.1007/978-3-319-61382-6_13
M3 - Conference contribution
AN - SCOPUS:85021769523
SN - 9783319613819
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 173
BT - Wired/Wireless Internet Communications - 15th IFIP WG 6.2 International Conference, WWIC 2017, Proceedings
A2 - Matta, Ibrahim
A2 - Koucheryavy, Yevgeni
A2 - Ometov, Aleksandr
A2 - Mamatas, Lefteris
A2 - Papadimitriou, Panagiotis
PB - Springer Verlag
T2 - 15th International Conference on Wired/Wireless Internet Communications, WWIC 2017
Y2 - 21 June 2017 through 23 June 2017
ER -
